SoroushVahidi/scidocs huggingface dataset:
Autonomous indoor object tracking with the Parrot AR.Drone
(2016)

This article presents an image-based visual servoing system for indoor visual tracking of 3D moving objects by an Unmanned Aerial Vehicle. This system autonomously follows a 3D moving target object, maintaining it with a fixed distance and centered on its image plane. The initial setup is tested in a detailed simulation environment. The system is then validated on flights in indoor scenarios using the Parrot AR.Drone and the CMT tracker, demonstrating the robustness of the system to differences in object features, environmental clutter, and target trajectory. The obtained results indicate that the proposed system is suitable for complex controls task, such object surveillance and pursuit.
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
(2018)

Deep learning is at the heart of the current rise of artificial intelligence. In the field of computer vision, it has become the workhorse for applications ranging from self-driving cars to surveillance and security. Whereas, deep neural networks have demonstrated phenomenal success (often beyond human capabilities) in solving complex problems, recent studies show that they are vulnerable to adversarial attacks in the form of subtle perturbations to inputs that lead a model to predict incorrect outputs. For images, such perturbations are often too small to be perceptible, yet they completely fool the deep learning models. Adversarial attacks pose a serious threat to the success of deep learning in practice. This fact has recently led to a large influx of contributions in this direction. This paper presents the first comprehensive survey on adversarial attacks on deep learning in computer vision. We review the works that design adversarial attacks, analyze the existence of such attacks and propose defenses against them. To emphasize that adversarial attacks are possible in practical conditions, we separately review the contributions that evaluate adversarial attacks in the real-world scenarios. Finally, drawing on the reviewed literature, we provide a broader outlook of this research direction.
Learning Category-Specific Deformable 3D Models for Object Reconstruction
(2017)

We address the problem of fully automatic object localization and reconstruction from a single image. This is both a very challenging and very important problem which has, until recently, received limited attention due to difficulties in segmenting objects and predicting their poses. Here we leverage recent advances in learning convolutional networks for object detection and segmentation and introduce a complementary network for the task of camera viewpoint prediction. These predictors are very powerful, but still not perfect given the stringent requirements of shape reconstruction. Our main contribution is a new class of deformable 3D models that can be robustly fitted to images based on noisy pose and silhouette estimates computed upstream and that can be learned directly from 2D annotations available in object detection datasets. Our models capture top-down information about the main global modes of shape variation within a class providing a “low-frequency” shape. In order to capture fine instance-specific shape details, we fuse it with a high-frequency component recovered from shading cues. A comprehensive quantitative analysis and ablation study on the PASCAL 3D+ dataset validates the approach as we show fully automatic reconstructions on PASCAL VOC as well as large improvements on the task of viewpoint prediction.
A Prism-Free Method for Silhouette Rendering in Inverse Displacement Mapping
(2008)

Silhouette is a key feature that distinguishes displacement mapping from normal mapping. However the silhouette rendering in the GPU implementation of displacement mapping (which is often called inversed displacement mapping) is tricky. Previous approaches rely mostly on construction of additional extruding prism-like geometry, which slows down the rendering significantly. In this paper, we proposed a method for solving the silhouette rendering problem in inverse displace mapping without using any extruding prism-like geometry. At each step of intersection finding, we continuously bends the viewing ray according to the current local tangent space associated with the surface. Thus, it allows mapping a displacement map onto an arbitrary curved surface with more accurate silhouette. While our method is simple, it offers surprisingly good results over Curved Relief Map (CRM) [OP05] in many difficult or degenerated cases.
Chemical Process Control Education and Practice
(2001)

Chemical process control textbooks and courses differ significantly from their electrical or mechanical-oriented brethren. It is our experience that colleagues in electrical engineering (EE) and mechanical engineering (ME) assume that we teach the same theory in our courses and merely have different application examples. The primary goals of this article are to i) emphasize the distinctly challenging characteristics of chemical processes, ii) present a typical process control curriculum, and iii) discuss how chemical process control courses can be revised to better meet the needs of a typical B.S.-level chemical engineer. In addition to a review of material covered in a standard process control course, we discuss innovative approaches in process control education, including the use of case studies, distributed control systems in laboratories, identification and control simulation packages, and studio-based approaches combining lecture, simulation, and experiments in the same room. We also provide perspectives on needed developments in process control education.
Solving the Search for Source Code
(2014)

Programmers frequently search for source code to reuse using keyword searches. The search effectiveness in facilitating reuse, however, depends on the programmer's ability to specify a query that captures how the desired code may have been implemented. Further, the results often include many irrelevant matches that must be filtered manually. More semantic search approaches could address these limitations, yet existing approaches are either not flexible enough to find approximate matches or require the programmer to define complex specifications as queries.
 We propose a novel approach to semantic code search that addresses several of these limitations and is designed for queries that can be described using a concrete input/output example. In this approach, programmers write lightweight specifications as inputs and expected output examples. Unlike existing approaches to semantic search, we use an SMT solver to identify programs or program fragments in a repository, which have been automatically transformed into constraints using symbolic analysis, that match the programmer-provided specification.
 We instantiated and evaluated this approach in subsets of three languages, the Java String library, Yahoo! Pipes mashup language, and SQL select statements, exploring its generality, utility, and trade-offs. The results indicate that this approach is effective at finding relevant code, can be used on its own or to filter results from keyword searches to increase search precision, and is adaptable to find approximate matches and then guide modifications to match the user specifications when exact matches do not already exist. These gains in precision and flexibility come at the cost of performance, for which underlying factors and mitigation strategies are identified.
Android Malware Detection Using Feature Fusion and Artificial Data
(2018)

For the Android malware detection / classification anti-malware community has relied on traditional malware detection methods as a countermeasure. However, traditional detection methods are developed for detecting the computer malware, which is different from Android malware in structure and characteristics. Thus, they may not be useful for Android malware detection. Moreover, majority of suggested detection approaches may not be generalized and are incapable of detecting zero-day malware due to different reasons such as available data set with specific set of examples. Thus, their detection accuracy may be questionable. To address this problem, this paper presents a malware classification approach with a reliable detection accuracy and evaluate the approach using artificially generated examples. The suggested approach generates the signature profiles and behavior profiles of each application in the data set, which are further used as input for the classification task. For improving the detection accuracy, feature fusion of features from filter methods and wrapper method and algorithm fusion is investigated. Without affecting the detection accuracy, the optimal balance between real world examples and synthetic examples is also investigated. The experimental results suggest that both AUC and F1 can be obtained up to 0.94 for both known and unknown malware using original examples and synthetic examples.
Service oriented architectures: approaches, technologies and research issues
(2007)

Service-oriented architectures (SOA) is an emerging approach that addresses the requirements of loosely coupled, standards-based, and protocol- independent distributed computing. Typically business operations running in an SOA comprise a number of invocations of these different components, often in an event-driven or asynchronous fashion that reflects the underlying business process needs. To build an SOA a highly distributable communications and integration backbone is required. This functionality is provided by the Enterprise Service Bus (ESB) that is an integration platform that utilizes Web services standards to support a wide variety of communications patterns over multiple transport protocols and deliver value-added capabilities for SOA applications. This paper reviews technologies and approaches that unify the principles and concepts of SOA with those of event-based programing. The paper also focuses on the ESB and describes a range of functions that are designed to offer a manageable, standards-based SOA backbone that extends middleware functionality throughout by connecting heterogeneous components and systems and offers integration services. Finally, the paper proposes an approach to extend the conventional SOA to cater for essential ESB requirements that include capabilities such as service orchestration, “intelligent” routing, provisioning, integrity and security of message as well as service management. The layers in this extended SOA, in short xSOA, are used to classify research issues and current research activities.
Survey on Energy Consumption Entities on the Smartphone Platform
(2011)

The full degree of freedom in mobile systems heavily depends on the energy provided by the mobile phone's batteries. Their capacity is in general limited and for sure not keeping pace as the mobile devices are crammed up with new functionalities. The discrepancy of Moore's law, offering twice the processing power at least each second year, and the development in batteries, which did not even double over the last decade, makes a shift in researchers' way of designing networks, protocols, and the mobile device itself. The bottleneck to take care of in the design process of mobile systems is not only the wireless data rate, but even more the energy limitation as the customers ask for new energy-hungry services, e.g., requiring faster connections or even multiple air interfaces, and longer standby or operational times of their mobile devices at the same time. In this survey, the energy consuming entities of a mobile device such as wireless air interfaces, display, mp3 player and others are measured and compared. The presented measurement results allow the reader to understand what the energy hungry parts of a mobile device are and use those findings for the design of future mobile protocols and applications. All results presented in this work and further results are made public on our web page [2].
Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior
(2000)

Reputation reporting systems have emerged as an important risk management mechanism in online trading communities. However, the predictive value of these systems can be compromised in situations where conspiring buyers intentionally give unfair ratings to sellers or, where sellers discriminate on the quality of service they provide to different buyers. This paper proposes and evaluates a set of mechanisms, which eliminate, or significantly reduce the negative effects of such fraudulent behavior. The proposed mechanisms can be easily integrated into existing online reputation systems in order to safeguard their reliability in the presence of potentially deceitful buyers and sellers.
Web services and business process management
(2002)

Web services based on the service-oriented architecture framework provide a suitable technical foundation for making business processes accessible within enterprises and across enterprises. But to appropriately support dynamic business processes and their management, more is needed, namely, the ability to prescribe how Web services are used to implement activities within a business process, how business processes are represented as Web services, and also which business partners perform what parts of the actual business process. In this paper, the relationship between Web services and the management of business processes is worked out and presented in a tutorial-like manner.
An implementation of cloud-based platform with R packages for spatiotemporal analysis of air pollution
(2017)

Recently, the R package has become a popular tool for big data analysis due to its several matured software packages for the data analysis and visualization, including the analysis of air pollution. The air pollution problem is of increasing global concern as it has greatly impacts on the environment and human health. With the rapid development of IoT and the increase in the accuracy of geographical information collected by sensors, a huge amount of air pollution data were generated. Thus, it is difficult to analyze the air pollution data in a single machine environment effectively and reliably due to its inherent characteristic of memory design. In this work, we construct a distributed computing environment based on both the softwares of RHadoop and SparkR for performing the analysis and visualization of air pollution with the R more reliably and effectively. In the work, we firstly use the sensors, called EdiGreen AirBox to collect the air pollution data in Taichung, Taiwan. Then, we adopt the Inverse Distance Weighting method to transform the sensors’ data into the density map. Finally, the experimental results show the accuracy of the short-term prediction results of PM2.5 by using the ARIMA model. In addition, the verification with respect to the prediction accuracy with the MAPE method is also presented in the experimental results.
Miniature Dual-Mode Microstrip Filters
(2007)

In order to reduce the size of dual-mode microstrip filters by keeping excellent performance, novel filter geometry is proposed. The novel filter includes a dual-mode resonator based on a meander structure. The effect of input/output feed lines located along a straight line on filter response is investigated for the dual-mode filter. The coupling between degenerate modes of the proposed dual-mode microstrip resonator is discussed depending on the perturbation size. The two dual-mode microstrip bandpass filters with real axis and imaginary axis transmission zeros (TZs) are designed, fabricated, and measured to document the validity of the description of the positive and negative coupling coefficient
EVS 25 Shenzhen , China , Nov . 5-9 , 2010 Control of A high-Performance Z-Source Inverter for Fuel Cell / Supercapacitor Hybrid Electric Vehicles
(2012)

This paper presents a supercapacitor (SC) module connected in parallel with fuel cell (FC) stack to supply a high-performance Z-Source Inverter (HP-ZSI) feeding a three phase induction motor for hybrid electric vehicles applications. The supercapacitor is connected between the input diode and the bidirectional switch of the highperformance ZSI topology. The indirect field-oriented control (IFOC) method is used to control an induction motor speed during motoring and regenerative braking operations to produce the modulation index and a dual loop controller is used to control the Z-network capacitor voltage to produce the shoot-through duty ratio. MATLAB simulation results verified the validity of the proposed control strategy during motoring and regenerative braking operations.
How do Retailers Adjust Prices?: Evidence from Store-Level Data
(2000)

Recent theoretical work on retail pricing dynamics suggests that retailers periodically hold sales periodic, temporary reductions in price, -even when their costs are unchanged. In this paper we extend existing theory to predict which items will go on sale, and use a new data set from the BLS to document the frequency of sales across a wide range of goods and geographic areas. We find a number of pricing regularities for the 20 categories of goods we examine. First, retailers seem to have a “regular” price, and most deviations from that price are downward. Second, there is considerable heterogeneity in sale behavior across goods within a category (e.g. cereal); the same items are regularly put on sale, while other items rarely are on sale. Third, items are more likely to go on sale when demand is highest. Fourth, for a limited number of items for which we know market shares, products with larger market shares go on sale more often. These final three observations are consistent with our theoretical result that popular products are most likely to be placed on sale.
Conditional Dynamic Mutual Information-Based Feature Selection
(2012)

With emergence of new techniques, data in many fields are getting larger and larger, especially in dimensionality aspect. The high dimensionality of data may pose great challenges to traditional learning algorithms. In fact, many of features in large volume of data are redundant and noisy. Their presence not only degrades the performance of learning algorithms, but also confuses end-users in the post-analysis process. Thus, it is necessary to eliminate irrelevant features from data before being fed into learning algorithms. Currently, many endeavors have been attempted in this field and many outstanding feature selection methods have been developed. Among different evaluation criteria, mutual information has also been widely used in feature selection because of its good capability of quantifying uncertainty of features in classification tasks. However, the mutual information estimated on the whole dataset can not exactly represent the correlation between features. To cope with this issue, in this paper we firstly re-estimate mutual information on identified instances dynamically, and then introduce a new feature selection method based on 1194 H. Liu, Y. Mo, J. Zhao conditional mutual information. Performance evaluations on sixteen UCI datasets show that our proposed method achieves comparable performance to other wellestablished feature selection algorithms in most cases.
Learning Sparse Feature Representations for Music Annotation and Retrieval
(2012)

We present a data-processing pipeline based on sparse feature learning and describe its applications to music annotation and retrieval. Content-based music annotation and retrieval systems process audio starting with features. While commonly used features, such as MFCC, are handcrafted to extract characteristics of the audio in a succinct way, there is increasing interest in learning features automatically from data using unsupervised algorithms. We describe a systemic approach applying feature-learning algorithms to music data, in particular, focusing on a highdimensional sparse-feature representation. Our experiments show that, using only a linear classifier, the newly learned features produce results on the CAL500 dataset comparable to state-of-the-art music annotation and retrieval systems.
Cost-Aware WWW Proxy Caching Algorithms
(1997)

Difference between Web Caching & Conventional Paging Problems: Web caching is variable-size caching (web documents vary dramatically in size depending on the information they carry (text, image, video, etc.). Web pages take different amounts of time to download, even if they are of the same size (download latency). Access streams seen by the proxy cache are the union of web access streams from tens to thousands of users (instead of coming from a few programmed sources as in the case of virtual memory paging)
Deep into the Brain: Artificial Intelligence in Stroke Imaging
(2017)

Artificial intelligence (AI), a computer system aiming to mimic human intelligence, is gaining increasing interest and is being incorporated into many fields, including medicine. Stroke medicine is one such area of application of AI, for improving the accuracy of diagnosis and the quality of patient care. For stroke management, adequate analysis of stroke imaging is crucial. Recently, AI techniques have been applied to decipher the data from stroke imaging and have demonstrated some promising results. In the very near future, such AI techniques may play a pivotal role in determining the therapeutic methods and predicting the prognosis for stroke patients in an individualized manner. In this review, we offer a glimpse at the use of AI in stroke imaging, specifically focusing on its technical principles, clinical application, and future perspectives.
Syntactic Stylometry: Using Sentence Structure for Authorship Attribution
(2012)

Most approaches to statistical stylometry have concentrated on lexical features, such as relative word frequencies or type-token ratios. Syntactic features have been largely ignored. This work attempts to fill that void by introducing a technique for authorship attribution based on dependency grammar. Syntactic features are extracted from texts using a common dependency parser, and those features are used to train a classifier to identify texts by author. While the method described does not outperform existing methods on most tasks, it does demonstrate that purely syntactic features carry information which could be useful for stylometric analysis. Index words: stylometry, authorship attribution, dependency grammar, machine learning Syntactic Stylometry: Using Sentence Structure for Authorship Attribution
Cooperative flood detection using GSMD via SMS
(2008)

This paper proposes architecture for an early warning floods system to alert public against flood disasters. An effective early warning system must be developed with linkages between four elements, which are accurate data collection to undertake risk assessments, development of hazard monitoring services, communication on risk related information and existence of community response capabilities. This project focuses on monitoring water level remotely using wireless sensor network. The project also utilizes Global System for Mobile communication (GSM) and short message service (SMS) to relay data from sensors to computers or directly alert the respective victim's through their mobile phone. It is hope that the proposed architecture can be further develop into a functioning system, which would be beneficial to the community and act as a precautionary action to save lives in the case of flood disaster.